NDLI: Locality-aware data replication in the last-level cache for large scale multicores

Content Provider	Springer Nature Link
Author	Hijaz, Farrukh Shi, Qingchuan Kurian, George Devadas, Srinivas Khan, Omer
Copyright Year	2016
Abstract	Next generation large single-chip multicores will process massive data with varying degree of locality. Harnessing on-chip data locality to optimize the utilization of on-chip cache and network resources is of fundamental importance. We propose a locality-aware selective data replication protocol for the last-level cache (LLC). The goal is to lower memory access latency and energy by only replicating cache lines with high reuse in the LLC slice of the requesting core, while simultaneously keep the off-chip miss rate low. The approach relies on low-overhead yet highly accurate in-hardware runtime cache line level classifier that only allows replication of cache lines with high reuse. Furthermore, a classifier captures the LLC pressure at the existing replica locations and adapts its replication decision accordingly. On a set of parallel benchmarks, the proposed protocol reduces overall energy by 14.7, 10.7, 10.5, and 16.7 % and completion time by 2.5, 6.5, 4.5, and 9.5 % when compared to the previously proposed Victim Replication, Adaptive Selective Replication, Reactive-NUCA, and Static-NUCA LLC management schemes. An efficient classifier implementation is evaluated with an overhead of 5.44 KB, which translates to only 1.58 % on top of the Static-NUCA baseline’s cache related per-core storage.
Starting Page	718
Ending Page	752
Page Count	35
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	72
Issue Number	2
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2016-02-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multicore Cache hierarchy Data management Energy efficiency Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture Software

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